Neural Fair Collaborative Filtering (NFCF)

This repository provides a simple demo for the NFCF model to debias career recommendations on the MovieLens data.

Prerequisites

Python
PyTorch

The code is tested on windows and linux operating systems. It should work on any other platform.

Instructions

Pre-processed MovieLens dataset is provided in "train-test" folder. The dataset contains interactions of users with movies and occupations.
Baseline model: To fine tune NCF model without any fairness interventions, run the code using "run_fine_tuning_typical_ncf_career_recommend.py" file. Training NFCF model:
Step-1 (pre-training): Run the code using "run_preTrainNCF.py" file. The pre-trained NCF model will be saved in "trained-models" folder.\
Step-2 (de-biasing embeddings): Run the code using "run_debiasing_userEmbeddings.py" file. The de-biased user embeddings will be saved in "results" folder.\
Step-3 (fine-tuning): Run the code using "run_nfcf_career_recommend.py" file. The model will be fine-tuned with necessary fairness interventions. Evaluation results on the test set will be saved in the "results" folder.

Author

Rashidul Islam (email: [email protected])

Reference Paper

R. Islam, K.N. Keya, Z. Zeng, S. Pan, and J.R. Foulds. Debiasing career recommendations with neural fair collaborative filtering. In Proceedings of the Web Conference (WWW), 2021..